Instructions to use mlx-community/MiniMax-M3.1-Flash-Text-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlx-community/MiniMax-M3.1-Flash-Text-4bit with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/MiniMax-M3.1-Flash-Text-4bit")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use mlx-community/MiniMax-M3.1-Flash-Text-4bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mlx-community/MiniMax-M3.1-Flash-Text-4bit"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "mlx-community/MiniMax-M3.1-Flash-Text-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use mlx-community/MiniMax-M3.1-Flash-Text-4bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "mlx-community/MiniMax-M3.1-Flash-Text-4bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "mlx-community/MiniMax-M3.1-Flash-Text-4bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "mlx-community/MiniMax-M3.1-Flash-Text-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use mlx-community/MiniMax-M3.1-Flash-Text-4bit with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mlx-community/MiniMax-M3.1-Flash-Text-4bit"

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default mlx-community/MiniMax-M3.1-Flash-Text-4bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use mlx-community/MiniMax-M3.1-Flash-Text-4bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mlx-community/MiniMax-M3.1-Flash-Text-4bit"

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "mlx-community/MiniMax-M3.1-Flash-Text-4bit" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

MiniMax-M3.1-Flash-Text-4bit / README.md

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---
license: other
license_name: minimax-community
license_link: LICENSE
library_name: mlx
pipeline_tag: text-generation
base_model: MiniMaxAI/MiniMax-M3.1-Flash
tags:
- moe
- agent
- coding
- mlx
---
# mlx-community/MiniMax-M3.1-Flash-Text-4bit

This model [mlx-community/MiniMax-M3.1-Flash-Text-4bit](https://huggingface.co/mlx-community/MiniMax-M3.1-Flash-Text-4bit) was converted to MLX format from [MiniMaxAI/MiniMax-M3.1-Flash](https://huggingface.co/MiniMaxAI/MiniMax-M3.1-Flash) using mlx-lm with [ml-explore/mlx-lm#2003](https://github.com/ml-explore/mlx-lm/pull/2003). Refer to the [original model card](https://huggingface.co/MiniMaxAI/MiniMax-M3.1-Flash) for more details on the model.

This is a text-only conversion. The vision tower is not included.

The weights keep the original quantization. The routed experts are NVFP4 with a per-expert global scale, the attention, shared expert and dense MLP layers are MXFP8, and the router stays in float32. Greedy output matched the original checkpoint token for token in testing. Peak memory was about 249 GB in a short generation test.

Tested with mlx-lm 0.32.2.

## Use with mlx

```bash
pip install -U mlx-lm
```

```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/MiniMax-M3.1-Flash-Text-4bit")
prompt = "hello"
if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True, return_dict=False,
    )
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```

	---
	license: other
	license_name: minimax-community
	license_link: LICENSE
	library_name: mlx
	pipeline_tag: text-generation
	base_model: MiniMaxAI/MiniMax-M3.1-Flash
	tags:
	- moe
	- agent
	- coding
	- mlx
	---

	# mlx-community/MiniMax-M3.1-Flash-Text-4bit

	This model [mlx-community/MiniMax-M3.1-Flash-Text-4bit](https://huggingface.co/mlx-community/MiniMax-M3.1-Flash-Text-4bit) was converted to MLX format from [MiniMaxAI/MiniMax-M3.1-Flash](https://huggingface.co/MiniMaxAI/MiniMax-M3.1-Flash) using mlx-lm with [ml-explore/mlx-lm#2003](https://github.com/ml-explore/mlx-lm/pull/2003). Refer to the [original model card](https://huggingface.co/MiniMaxAI/MiniMax-M3.1-Flash) for more details on the model.

	This is a text-only conversion. The vision tower is not included.

	The weights keep the original quantization. The routed experts are NVFP4 with a per-expert global scale, the attention, shared expert and dense MLP layers are MXFP8, and the router stays in float32. Greedy output matched the original checkpoint token for token in testing. Peak memory was about 249 GB in a short generation test.

	Tested with mlx-lm 0.32.2.

	## Use with mlx

	```bash
	pip install -U mlx-lm
	```

	```python
	from mlx_lm import load, generate

	model, tokenizer = load("mlx-community/MiniMax-M3.1-Flash-Text-4bit")

	prompt = "hello"

	if tokenizer.chat_template is not None:
	messages = [{"role": "user", "content": prompt}]
	prompt = tokenizer.apply_chat_template(
	messages, add_generation_prompt=True, return_dict=False,
	)

	response = generate(model, tokenizer, prompt=prompt, verbose=True)
	```